“Carotenoid analysis of cassava genotypes roots (Manihot esculenta Crantz) cultivated in southern Brazil using chemometric tools” Author: “Moresco, R.(2014)” Date: “Thursday, January 22, 2015”

R script for Analysis of HPLC and UV-Visible Spectrophotometric Data

Reading data and metadata

setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
source("http://bioconductor.org/biocLite.R")
source("scripts/init.R")
uv.cassava.metadata.file = "Datasets/CassavaCultivars/UVV/metadata/cass_uv_metadata.csv"
uv.cassava.data.file = "Datasets/CassavaCultivars/UVV/data/uvv-cassava.csv"

label.x = "wavelength(nm)"
label.val = "absorbance"
uv.cassava.ds = read.dataset.csv(uv.cassava.data.file, uv.cassava.metadata.file, 
                           description = "UV data for cassava", type = "uvv-spectra", format = "col",
                                 label.x = label.x, label.values = label.val)

Preliminary Inspection of Data

sum.dataset(uv.cassava.ds)
## Dataset summary:
## Valid dataset
## Description:  UV data for cassava 
## Type of data:  uvv-spectra 
## Number of samples:  30 
## Number of data points 501 
## Number of metadata variables:  3 
## Label of x-axis values:  wavelength(nm) 
## Label of data points:  absorbance 
## Number of missing values in data:  0 
## Mean of data values:  0.1605 
## Median of data values:  0.01487 
## Standard deviation:  0.4252 
## Range of values:  -0.1068 2.722 
## Quantiles: 
##         0%        25%        50%        75%       100% 
## -0.1067503 -0.0005422  0.0148732  0.0888727  2.7218540

Get metadata

get.metadata(uv.cassava.ds)
##                   varieties colors replicates
## Apronta mesa_1 Apronta.mesa  cream          1
## Apronta mesa_2 Apronta.mesa  cream          2
## Apronta mesa_3 Apronta.mesa  cream          3
## Pioneira_1         Pioneira yellow          1
## Pioneira_2         Pioneira yellow          2
## Pioneira_3         Pioneira yellow          3
## Oriental_1         Oriental  cream          1
## Oriental_2         Oriental  cream          2
## Oriental_3         Oriental  cream          3
## Amarela_1           Amarela yellow          1
## Amarela_2           Amarela yellow          2
## Amarela_3           Amarela yellow          3
## Catarina_1         Catarina yellow          1
## Catarina_2         Catarina yellow          2
## Catarina_3         Catarina yellow          3
## IAC 576-70_1     IAC.576.70 yellow          1
## IAC 576-70_2     IAC.576.70 yellow          2
## IAC 576-70_3     IAC.576.70 yellow          3
## Salezio_1           Salezio  cream          1
## Salezio_2           Salezio  cream          2
## Salezio_3           Salezio  cream          3
## Estacao_1           Estacao  cream          1
## Estacao_2           Estacao  cream          2
## Estacao_3           Estacao  cream          3
## Videira_1           Videira  cream          1
## Videira_2           Videira  cream          2
## Videira_3           Videira  cream          3
## Rosada_1             Rosada    red          1
## Rosada_2             Rosada    red          2
## Rosada_3             Rosada    red          3

USING FULL UV-VISIBLE DATA (200-700 nm)

plot.spectra(uv.cassava.ds,"varieties")

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Data Pre-Processing

Smoothing and baseline correction

uv.cassava.wavelens = get.x.values.as.num(uv.cassava.ds)
x.axis.sm = seq(min(uv.cassava.wavelens), max(uv.cassava.wavelens),10)
uv.cassava.smooth = smoothing.interpolation(uv.cassava.ds, method = "loess", x.axis = x.axis.sm)
plot.spectra(uv.cassava.smooth, "varieties")

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uv.cassava.bg = data.correction(uv.cassava.smooth,"background")
uv.cassava.offset = data.correction(uv.cassava.bg, "offset")
uv.cassava.baseline = data.correction(uv.cassava.offset, "baseline")
sum.dataset(uv.cassava.baseline)
## Dataset summary:
## Valid dataset
## Description:  UV data for cassava-smoothed with hyperSpec spc.loess; background correction; offset correction; baseline correction 
## Type of data:  undefined 
## Number of samples:  30 
## Number of data points 51 
## Number of metadata variables:  3 
## Label of x-axis values:  wavelength(nm) 
## Label of data points:  absorbance 
## Number of missing values in data:  0 
## Mean of data values:  0.08889 
## Median of data values:  0.02441 
## Standard deviation:  0.1923 
## Range of values:  -0.0002181 1.29 
## Quantiles: 
##         0%        25%        50%        75%       100% 
## -0.0002181  0.0076378  0.0244131  0.0764408  1.2895338
plot.spectra(uv.cassava.baseline, "varieties")

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UNIVARIATE ANALYSIS

uv.cassava.baseline.anova = univariate.analysis(uv.cassava.baseline, type = "anova", "varieties")
uv.cassava.baseline.anova[1:10,]
##       pvalues  logs       fdr
## 500 1.073e-18 17.97 5.470e-17
## 470 4.485e-18 17.35 1.144e-16
## 490 1.394e-17 16.86 2.083e-16
## 460 1.642e-17 16.78 2.083e-16
## 510 2.043e-17 16.69 2.083e-16
## 480 6.334e-17 16.20 5.384e-16
## 290 7.563e-17 16.12 5.510e-16
## 440 3.299e-16 15.48 2.103e-15
## 450 4.829e-16 15.32 2.736e-15
## 300 1.322e-15 14.88 6.740e-15
##                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                                            tukey
## 500                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 470                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 490                                                                                                                                                                                                                                                                                                                                                                                                                                                   Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-Salezio
## 460                                                                                                                                                                                                                                                                                                   Rosada-Amarela; Catarina-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 510                                                                                                                                                                                                                                                                                                                                                                                                                                                                    Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 480                                                                                                                                                                                                                                                                                                                                                                                                                                                  Rosada-Amarela; Rosada-Apronta.mesa; Rosada-Catarina; Salezio-Catarina; Rosada-Estacao; Rosada-IAC.576.70; Rosada-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 290                                    Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Rosada; Videira-Rosada
## 440                                                                                                                                                         Apronta.mesa-Amarela; Estacao-Amarela; Oriental-Amarela; Rosada-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 450                                                                                                                                                                               Oriental-Amarela; Rosada-Amarela; Salezio-Amarela; Catarina-Apronta.mesa; IAC.576.70-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Estacao-Catarina; Oriental-Catarina; Rosada-Catarina; Salezio-Catarina; IAC.576.70-Estacao; Pioneira-Estacao; Rosada-Estacao; Oriental-IAC.576.70; Rosada-IAC.576.70; Salezio-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada
## 300 Apronta.mesa-Amarela; IAC.576.70-Amarela; Oriental-Amarela; Pioneira-Amarela; Salezio-Amarela; Videira-Amarela; Catarina-Apronta.mesa; Estacao-Apronta.mesa; Pioneira-Apronta.mesa; Rosada-Apronta.mesa; Videira-Apronta.mesa; IAC.576.70-Catarina; Oriental-Catarina; Pioneira-Catarina; Salezio-Catarina; Videira-Catarina; IAC.576.70-Estacao; Oriental-Estacao; Pioneira-Estacao; Salezio-Estacao; Videira-Estacao; Pioneira-IAC.576.70; Rosada-IAC.576.70; Videira-IAC.576.70; Pioneira-Oriental; Rosada-Oriental; Videira-Oriental; Rosada-Pioneira; Salezio-Pioneira; Salezio-Rosada; Videira-Rosada; Videira-Salezio

Hierarchical Cluster Analysis

Using Euclidian Distance

uv.cassava.hc = clustering(uv.cassava.ds, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.ds, uv.cassava.hc)

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dendrogram.plot.col(uv.cassava.ds, uv.cassava.hc, "varieties")

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Principal Components Analysis

Importance of components: Proportion of Variance explained in each component

uv.cassava.pca = pca.analysis.dataset(uv.cassava.ds)
summary(uv.cassava.pca)
## Importance of components:
##                           PC1    PC2   PC3   PC4    PC5     PC6     PC7
## Standard deviation     17.129 10.087 8.289 4.249 3.0243 1.77968 1.31468
## Proportion of Variance  0.586  0.203 0.137 0.036 0.0183 0.00632 0.00345
## Cumulative Proportion   0.586  0.789 0.926 0.962 0.9802 0.98651 0.98996
##                            PC8     PC9    PC10    PC11    PC12    PC13
## Standard deviation     0.99687 0.84476 0.77221 0.72841 0.63003 0.47799
## Proportion of Variance 0.00198 0.00142 0.00119 0.00106 0.00079 0.00046
## Cumulative Proportion  0.99194 0.99337 0.99456 0.99562 0.99641 0.99686
##                           PC14    PC15    PC16    PC17    PC18    PC19
## Standard deviation     0.43455 0.41855 0.40385 0.36874 0.33393 0.32915
## Proportion of Variance 0.00038 0.00035 0.00033 0.00027 0.00022 0.00022
## Cumulative Proportion  0.99724 0.99759 0.99792 0.99819 0.99841 0.99863
##                           PC20   PC21    PC22    PC23    PC24    PC25
## Standard deviation     0.32294 0.3174 0.29612 0.28055 0.26165 0.25277
## Proportion of Variance 0.00021 0.0002 0.00018 0.00016 0.00014 0.00013
## Cumulative Proportion  0.99883 0.9990 0.99921 0.99937 0.99950 0.99963
##                           PC26   PC27    PC28    PC29     PC30
## Standard deviation     0.23929 0.2257 0.20367 0.18610 7.61e-15
## Proportion of Variance 0.00011 0.0001 0.00008 0.00007 0.00e+00
## Cumulative Proportion  0.99975 0.9999 0.99993 1.00000 1.00e+00

Robust and centralized pca (3D and 2D)

pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses=T)

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses=T, pallette=2)

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "varieties", ellipses="F")

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="T")

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pca.scoresplot2D(uv.cassava.ds, uv.cassava.pca, "colors", ellipses="T", pallette=2, labels="F")

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CAROTENOIDS FINGERPRINT REGION (400-500 nm)

Carotenoids have absorption maxima in the UV-visible region of 450 nm

uv.cassava.carot = subset.x.values.by.interval(uv.cassava.ds, min.value = 400, max.value = 500)
sum.dataset(uv.cassava.carot)
## Dataset summary:
## Valid dataset
## Description:  UV data for cassava 
## Type of data:  uvv-spectra 
## Number of samples:  30 
## Number of data points 101 
## Number of metadata variables:  3 
## Label of x-axis values:  wavelength(nm) 
## Label of data points:  absorbance 
## Number of missing values in data:  0 
## Mean of data values:  0.127 
## Median of data values:  0.02827 
## Standard deviation:  0.2668 
## Range of values:  -0.0228 1.498 
## Quantiles: 
##       0%      25%      50%      75%     100% 
## -0.02280  0.01033  0.02827  0.10888  1.49789

Plotting spectra

plot.spectra(uv.cassava.carot, "varieties", legend="topleft")

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Principal Components Analysis

Importance of components: Proportion of Variance explained in each component

uv.cassava.carot.pca = pca.analysis.dataset(uv.cassava.carot, scale = T, center = T)
summary(uv.cassava.carot.pca)
## Importance of components:
##                           PC1     PC2    PC3     PC4     PC5   PC6     PC7
## Standard deviation     10.012 0.86277 0.1417 0.05031 0.02480 0.014 0.00975
## Proportion of Variance  0.992 0.00737 0.0002 0.00003 0.00001 0.000 0.00000
## Cumulative Proportion   0.992 0.99977 1.0000 0.99999 1.00000 1.000 1.00000
##                            PC8     PC9    PC10    PC11  PC12    PC13
## Standard deviation     0.00782 0.00479 0.00384 0.00307 0.003 0.00221
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.000 0.00000
## Cumulative Proportion  1.00000 1.00000 1.00000 1.00000 1.000 1.00000
##                           PC14    PC15    PC16    PC17    PC18    PC19
## Standard deviation     0.00207 0.00197 0.00189 0.00175 0.00169 0.00158
## Proportion of Variance 0.00000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion  1.00000 1.00000 1.00000 1.00000 1.00000 1.00000
##                          PC20    PC21    PC22    PC23    PC24    PC25
## Standard deviation     0.0015 0.00139 0.00133 0.00124 0.00116 0.00112
## Proportion of Variance 0.0000 0.00000 0.00000 0.00000 0.00000 0.00000
## Cumulative Proportion  1.0000 1.00000 1.00000 1.00000 1.00000 1.00000
##                          PC26     PC27     PC28     PC29     PC30
## Standard deviation     0.0011 0.000965 0.000929 0.000818 9.93e-16
## Proportion of Variance 0.0000 0.000000 0.000000 0.000000 0.00e+00
## Cumulative Proportion  1.0000 1.000000 1.000000 1.000000 1.00e+00

PCAs Graphics

pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="F")

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pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "varieties", ellipses="T")

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pca.scoresplot2D(uv.cassava.carot, uv.cassava.carot.pca, pcas = c(1,2), "colors", labels="F", pallette=2, ellipses="T")

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Hierarchical Cluster Analysis

uv.cassava.carot.hc = clustering(uv.cassava.carot, method = "hc", distance = "euclidean")
dendrogram.plot(uv.cassava.carot, uv.cassava.carot.hc)

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dendrogram.plot.col(uv.cassava.carot, uv.cassava.hc, "colors")

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Profile and Quantification of Carotenoids using High Performance Liquid Chromatography (HPLC)

Subsequent analysis was performed to characterize the carotenoids by HPLC. The chromatographic analysis identified the cis-beta- and trans-beta-carotene, beta-carotene, lutein and beta-cryptoxanthin in all genotypes analyzed, confirmed the presence of lycopene only in Rosada genotype. Trans-beta-carotene was the major component in all samples.

setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
load("hplcrodolfo.RData") 
hplcrodolfo
##       Cultivar Lutein ßCryptoxanthin aCarotene cisßcarotene Transßcarotene
## 1  Aprontamesa  0.091          0.109     0.000        0.000          0.000
## 2     Pioneira  0.319          0.071     0.306        2.967          3.425
## 3     Oriental  0.052          0.103     0.000        0.109          0.123
## 4      Amarela  0.685          0.033     0.043        3.292          4.224
## 5     Catarina  0.357          0.076     0.198        4.770          5.797
## 6     IAC57670  0.688          0.000     0.664        5.826          6.420
## 7      Salezio  0.055          0.066     0.328        0.065          0.354
## 8      Estacao  0.058          0.084     0.435        0.254          0.328
## 9      Videira  0.070          0.110     0.000        0.039          0.340
## 10      Rosada  0.511          0.605     4.732        4.480        166.296
##    Lycopene
## 1     0.000
## 2     0.000
## 3     0.000
## 4     0.000
## 5     0.000
## 6     0.000
## 7     0.000
## 8     0.000
## 9     0.000
## 10    1.534
cultivar=factor(hplcrodolfo$Cultivar)  
cultivar
##  [1] Aprontamesa Pioneira    Oriental    Amarela     Catarina   
##  [6] IAC57670    Salezio     Estacao     Videira     Rosada     
## 10 Levels: Amarela Aprontamesa Catarina Estacao IAC57670 ... Videira
hplc<-hplcrodolfo[2:7]  

Apply function of ade4

require(ade4)
## Loading required package: ade4
## Warning: package 'ade4' was built under R version 3.1.2
HPLC <- dudi.pca(hplc, center = TRUE, scale = TRUE, scan = F,nf=5)
summary(HPLC)   ##summarize the function
## Class: pca dudi
## Call: dudi.pca(df = hplc, center = TRUE, scale = TRUE, scannf = F, 
##     nf = 5)
## 
## Total inertia: 6
## 
## Eigenvalues:
##       Ax1       Ax2       Ax3       Ax4       Ax5 
## 4.2593319 1.6109364 0.1050266 0.0240342 0.0006663 
## 
## Projected inertia (%):
##      Ax1      Ax2      Ax3      Ax4      Ax5 
## 70.98887 26.84894  1.75044  0.40057  0.01111 
## 
## Cumulative projected inertia (%):
##     Ax1   Ax1:2   Ax1:3   Ax1:4   Ax1:5 
##   70.99   97.84   99.59   99.99  100.00 
## 
## (Only 5 dimensions (out of 6) are shown)
HPLC$eig       ##eigenvalues (variability in the data)
## [1] 4.259e+00 1.611e+00 1.050e-01 2.403e-02 6.663e-04 4.587e-06
HPLC$li        ##row cordinates
##      Axis1   Axis2     Axis3     Axis4      Axis5
## 1   1.0560 -0.9772  0.140625 -0.118353  0.0217429
## 2   0.4634  0.5304 -0.188201 -0.015570  0.0249376
## 3   1.0947 -1.0398 -0.001810 -0.093596 -0.0219527
## 4   0.2746  1.6941  0.742037 -0.111167 -0.0098409
## 5   0.2188  1.1486 -0.653985 -0.205050 -0.0173457
## 6  -0.1705  2.4414 -0.090789  0.220190  0.0069017
## 7   1.0822 -1.0046  0.007296  0.233256 -0.0540229
## 8   0.9711 -0.9825 -0.052789  0.213374  0.0401974
## 9   1.0649 -1.0249  0.070085 -0.120416  0.0103131
## 10 -6.0551 -0.7856  0.027531 -0.002668 -0.0009305
HPLC$l1        ##row normed cordinates
##        RS1     RS2       RS3      RS4      RS5
## 1   0.5117 -0.7699  0.433923 -0.76342  0.84230
## 2   0.2246  0.4179 -0.580727 -0.10043  0.96606
## 3   0.5304 -0.8193 -0.005586 -0.60373 -0.85043
## 4   0.1330  1.3348  2.289686 -0.71707 -0.38123
## 5   0.1060  0.9050 -2.017986 -1.32265 -0.67196
## 6  -0.0826  1.9236 -0.280147  1.42031  0.26737
## 7   0.5244 -0.7915  0.022514  1.50459 -2.09281
## 8   0.4705 -0.7741 -0.162891  1.37634  1.55722
## 9   0.5160 -0.8075  0.216261 -0.77673  0.39952
## 10 -2.9340 -0.6189  0.084953 -0.01721 -0.03605
HPLC$co     ##column cordinates (correlations between variables and pcs)
##                  Comp1   Comp2    Comp3     Comp4      Comp5
## Lutein         -0.4767  0.8499  0.22431 -0.007895  0.0033189
## ßCryptoxanthin -0.9231 -0.3718 -0.00572 -0.096675  0.0147035
## aCarotene      -0.9830 -0.1375 -0.02382  0.119013  0.0105172
## cisßcarotene   -0.5326  0.8142 -0.23026 -0.018924 -0.0006946
## Transßcarotene -0.9867 -0.1608  0.01694 -0.008404 -0.0135522
## Lycopene       -0.9780 -0.2063  0.02832 -0.005738 -0.0120158
HPLC$c1    ##column normed scores (loadings)
##                    CS1     CS2      CS3      CS4      CS5
## Lutein         -0.2310  0.6697  0.69216 -0.05093  0.12857
## ßCryptoxanthin -0.4473 -0.2930 -0.01765 -0.62359  0.56960
## aCarotene      -0.4763 -0.1083 -0.07350  0.76768  0.40743
## cisßcarotene   -0.2581  0.6415 -0.71052 -0.12207 -0.02691
## Transßcarotene -0.4781 -0.1267  0.05226 -0.05421 -0.52500
## Lycopene       -0.4739 -0.1625  0.08738 -0.03701 -0.46548

Plot PCA

biplot(HPLC)

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scatter(HPLC,clab.row = 0, posieig = "none",col = as.numeric(cultivar))
## NULL
plot.new=T
s.class(HPLC$li, fac = cultivar, col =as.numeric(cultivar), add.plot = TRUE, cstar = 0, clabel = 1,
cellipse = 0,pch =23,addaxes=TRUE,cpoint=1)

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Magnification (zoom) for the overlapped samples

s.class(HPLC$li, fac = cultivar, col =as.numeric(cultivar), cstar = 0, clabel = 1,
cellipse = 0,pch =23,addaxes=TRUE,cpoint=1, xlim = c(1,1.1), ylim = c(-1.1,-0.9))

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Cluster Analisys (HPLC data)

Similarity of cassava genotypes in respect to their carotenoid composition determined by RP-HPLC.

setwd("/Users/Windows/Desktop/Miguel/Metabolomics-package")
library(vegan)
Carotenoids_HPLC_Rodolfo <- read.table("Carotenoids_HPLC_Rodolfo.txt", header=TRUE, dec=",")
Carotenoids_HPLC_Rodolfo
# standardization of data
Carotenoids_HPLC_Rodolfo.z <- decostand(Carotenoids_HPLC_Rodolfo, "standardize", MARGIN=2)
Carotenoids_HPLC_Rodolfo.z
standard=Carotenoids_HPLC_Rodolfo.z
standard
library(clustsig)

Hierarchical cluster dendogram analysis (UPGMA method) The similarity between members of the same cluster is statistically significant, when the branches in the dendrogram show the same color. Significance determined by Simprof analysis (Similiarity Profile Analysis) from R Clustsig package in accordance with Clarke, Somerfield & Gorley, (2008)

diststandard= dist(standard, method = "euclidean")
hcstandard=hclust(diststandard)
plot(hcstandard)

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Carotenoids_HPLC_RodolfoSIMPROF <- simprof(data=Carotenoids_HPLC_Rodolfo, method.distance="euclidean") 
simprof.plot(Carotenoids_HPLC_RodolfoSIMPROF) 

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## 'dendrogram' with 2 branches and 10 members total, at height 164.1